# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
!pip install pandas-profiling #EDA
from pandas_profiling import ProfileReport
**Problem Statement**
The sinking of the Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.
While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.
In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).
# Scientific Libraries
import pandas as pd
import numpy as np
# Visualization
import matplotlib.pyplot as plt
import matplotlib.style as style
import seaborn as sns
style.use('fivethirtyeight')
# Plotly
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
# Booster for rendering
import plotly.io as pio
pio.renderers.default = 'iframe'
# Warning ignorance
import warnings
warnings.filterwarnings('ignore')
# Scaling
from sklearn.preprocessing import MinMaxScaler, StandardScaler
# Selection
from scipy.stats import chi2_contingency
# Machine Learning Algorithm
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from xgboost import XGBClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score,StratifiedKFold
# Metrics Evaluation
from sklearn.metrics import accuracy_score, precision_score
from sklearn.metrics import recall_score, f1_score
from sklearn.metrics import roc_curve, auc, confusion_matrix
from sklearn.metrics import classification_report
from sklearn.metrics import plot_confusion_matrix
# Hyperparameter Tuning
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
# Import Train Dataset
train = pd.read_csv('/kaggle/input/titanic/train.csv')
# Import Test Dataset
test = pd.read_csv('/kaggle/input/titanic/test.csv')
df = pd.DataFrame(data=train)
pr = ProfileReport(df)
pr.to_file(output_file='pandas_profiling.html')
pr